Prolonged Sleep Restriction Affects Glucose Metabolism in Healthy Young Men

Peer reviewe

BAKTRAK: Backtracking drifting objects using an iterative algorithm with a forward trajectory model

The task of determining the origin of a drifting object after it has been located is highly complex due to the uncertainties in drift properties and environmental forcing (wind, waves and surface currents). Usually the origin is inferred by running a trajectory model (stochastic or deterministic) in reverse. However, this approach has some severe drawbacks, most notably the fact that many drifting objects go through nonlinear state changes underway (e.g., evaporating oil or a capsizing lifeboat). This makes it difficult to naively construct a reverse-time trajectory model which realistically predicts the earliest possible time the object may have started drifting. We propose instead a different approach where the original (forward) trajectory model is kept unaltered while an iterative seeding and selection process allows us to retain only those particles that end up within a certain time-space radius of the observation. An iterative refinement process named BAKTRAK is employed where those trajectories that do not make it to the goal are rejected and new trajectories are spawned from successful trajectories. This allows the model to be run in the forward direction to determine the point of origin of a drifting object. The method is demonstrated using the Leeway stochastic trajectory model for drifting objects due to its relative simplicity and the practical importance of being able to identify the origin of drifting objects. However, the methodology is general and even more applicable to oil drift trajectories, drifting ships and hazardous material that exhibit non-linear state changes such as evaporation, chemical weathering, capsizing or swamping. The backtracking method is tested against the drift trajectory of a life raft and is shown to predict closely the initial release position of the raft and its subsequent trajectory.Comment: 28 pages, 8 figures, 2 table

Physiological and autonomic stress responses after prolonged sleep restriction and subsequent recovery sleep in healthy young men

Purpose Sleep restriction is increasingly common and associated with the development of health problems. We investigated how the neuroendocrine stress systems respond to prolonged sleep restriction and subsequent recovery sleep in healthy young men. Methods After two baseline (BL) nights of 8 h time in bed (TIB), TIB was restricted to 4 h per night for five nights (sleep restriction, SR, n = 15), followed by three recovery nights (REC) of 8 h TIB, representing a busy workweek and a recovery weekend. The control group (n = 8) had 8 h TIB throughout the experiment. A variety of autonomic cardiovascular parameters, together with salivary neuropeptide Y (NPY) and cortisol levels, were assessed. Results In the control group, none of the parameters changed. In the experimental group, heart rate increased from 60 +/- 1.8 beats per minute (bpm) at BL, to 63 +/- 1.1 bpm after SR and further to 65 +/- 1.8 bpm after REC. In addition, whole day low-frequency to-high frequency (LF/HF) power ratio of heart rate variability increased from 4.6 +/- 0.4 at BL to 6.0 +/- 0.6 after SR. Other parameters, including salivary NPY and cortisol levels, remained unaffected. Conclusions Increased heart rate and LF/HF power ratio are early signs of an increased sympathetic activity after prolonged sleep restriction. To reliably interpret the clinical significance of these early signs of physiological stress, a follow-up study would be needed to evaluate if the stress responses escalate and lead to more unfavourable reactions, such as elevated blood pressure and a subsequent elevated risk for cardiovascular health problems.Peer reviewe

A quantitative comparison of different methods to detect cardiorespiratory coordination during night-time sleep

BACKGROUND: The univariate approaches used to analyze heart rate variability have recently been extended by several bivariate approaches with respect to cardiorespiratory coordination. Some approaches are explicitly based on mathematical models which investigate the synchronization between weakly coupled complex systems. Others use an heuristic approach, i.e. characteristic features of both time series, to develop appropriate bivariate methods. OBJECTIVE: In this study six different methods used to analyze cardiorespiratory coordination have been quantitatively compared with respect to their performance (no. of sequences with cardiorespiratory coordination, no. of heart beats coordinated with respiration). Five of these approaches have been suggested in the recent literature whereas one method originates from older studies. RESULTS: The methods were applied to the simultaneous recordings of an electrocardiogram and a respiratory trace of 20 healthy subjects during night-time sleep from 0:00 to 6:00. The best temporal resolution and the highest number of coordinated heart beats were obtained with the analysis of 'Phase Recurrences'. Apart from the oldest method, all methods showed similar qualitative results although the quantities varied between the different approaches. In contrast, the oldest method detected considerably fewer coordinated heart beats since it only used part of the maximum amount of information available in each recording. CONCLUSIONS: The method of 'Phase Recurrences' should be the method of choice for the detection of cardiorespiratory coordination since it offers the best temporal resolution and the highest number of coordinated sequences and heart beats. Excluding the oldest method, the results of the heuristic approaches may also be interpreted in terms of the mathematical models

Inflammatory cell content of coronary thrombi is dependent on thrombus age in patients with ST-elevation myocardial infarction

BACKGROUND: ST-elevation myocardial infarction (STEMI) is typically caused by an occlusive coronary thrombus. The process of intracoronary thrombus formation is poorly understood. It is known that inflammatory cells play a role in the formation and resolution of venous thrombi, however their role in coronary thrombosis is not clear. We therefore analyzed inflammatory cells in thrombi derived from patients with STEMI in relation to histologically classified thrombus age. METHODS: Thrombus aspirates of 113 patients treated with primary percutaneous coronary intervention were prospectively collected and classified (fresh, lytic, or organized) based on hematoxylin and eosin staining. The density of inflammatory cells neutrophils (MPO), monocytes/macrophages (CD68), lymphocytes (CD45), and the platelet area (CD31), were visualized using immunohistochemistry. Patients' history, medication, and laboratory data were registered. RESULTS: Fresh thrombi (76.1%) were the most abundant as compared to lytic (16.8%) and organized (7.1%) thrombi. Neutrophils were significantly less present in organized (169cells/mm(2)) compared to fresh (327 cells/mm(2)) and lytic thrombi (311 cells/mm(2)). Monocytes/macrophages were significantly more present in lytic (471 cells/mm(2)) than in fresh (312 cells/mm(2)) thrombi. We additionally found that thrombi from patients aged 50 years old contained significantly more neutrophils and monocytes/macrophages irrespective of thrombus age. Furthermore platelet area was smaller in patients on aspirin again irrespective of thrombus age. No gender differences were found. CONCLUSIONS: The composition of inflammatory cells differs with thrombus age in thrombosuction material of STEMI patients that in part depends on patient age and medication

Partial Sleep Restriction Activates Immune Response-Related Gene Expression Pathways: Experimental and Epidemiological Studies in Humans

Epidemiological studies have shown that short or insufficient sleep is associated with increased risk for metabolic diseases and mortality. To elucidate mechanisms behind this connection, we aimed to identify genes and pathways affected by experimentally induced, partial sleep restriction and to verify their connection to insufficient sleep at population level. The experimental design simulated sleep restriction during a working week: sleep of healthy men (N = 9) was restricted to 4 h/night for five nights. The control subjects (N = 4) spent 8 h/night in bed. Leukocyte RNA expression was analyzed at baseline, after sleep restriction, and after recovery using whole genome microarrays complemented with pathway and transcription factor analysis. Expression levels of the ten most up-regulated and ten most down-regulated transcripts were correlated with subjective assessment of insufficient sleep in a population cohort (N = 472). Experimental sleep restriction altered the expression of 117 genes. Eight of the 25 most up-regulated transcripts were related to immune function. Accordingly, fifteen of the 25 most up-regulated Gene Ontology pathways were also related to immune function, including those for B cell activation, interleukin 8 production, and NF-κB signaling (P<0.005). Of the ten most up-regulated genes, expression of STX16 correlated negatively with self-reported insufficient sleep in a population sample, while three other genes showed tendency for positive correlation. Of the ten most down-regulated genes, TBX21 and LGR6 correlated negatively and TGFBR3 positively with insufficient sleep. Partial sleep restriction affects the regulation of signaling pathways related to the immune system. Some of these changes appear to be long-lasting and may at least partly explain how prolonged sleep restriction can contribute to inflammation-associated pathological states, such as cardiometabolic diseases

Expression changes after partial sleep restriction.

<p>The 310 entities (genes/transcripts) with interaction <i>P</i> value (<i>P</i><0.05) in 2-way ANOVA, sorted by average fold change from baseline (BL) to sleep restriction (SR) (with the up-regulated (red) on top, followed by the down-regulated (green). Each lane represents one individual (sleep deprived subjects, N = 9; controls, N = 4), and colour codes represent the fold change from BL to SR (BL  = 1).</p